In the past decade the proposed *SGM approach [1]} demonstrated a good trade-off between runtime and accuracy in numerous studies.
It uses dynamic programming to optimize a global energy function by aggregation the cost of each pixel \(\mathrm {p}\)  given the disparity \(d_\mathrm {p}\)  along multiple one-dimensional paths that cover the complete image.
Hereby, each path computation is an independent sub-problem, making *SGM suitable for highly parallel execution on dedicated hardware.
While it is initially proposed to use 16 concentric paths, most studies done on conventional hardware only use eight paths in order to avoid subpixel image access.
However, due to the limited amount on memory on embedded FPGAs and their strengths in processing data streams, it is common practice to only use four paths when porting the algorithm onto such hardware (cf. sec:sgm).
Studies show that a reduction from eight to four paths lead to a loss in accuracy of 1.7% while greatly increasing the performance of the algorithm [2]}.
